# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import unittest
from functools import partial
from itertools import product
from typing import TYPE_CHECKING, Any

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest

import paddle.inference as paddle_infer

if TYPE_CHECKING:
    from collections.abc import Generator


class TrtConvertStridedSliceTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]]):
            return np.random.random([1, 56, 56, 192]).astype(np.float32)

        for axes, starts, ends, decrease_axis, infer_flags, strides in product(
            [[1, 2]],
            [[1, 1]],
            [[10000000, 10000000]],
            [[]],
            [[1, 1]],
            [[2, 2]],
        ):
            dics = [
                {
                    "axes": axes,
                    "starts": starts,
                    "ends": ends,
                    "decrease_axis": decrease_axis,
                    "infer_flags": infer_flags,
                    "strides": strides,
                }
            ]

            ops_config = [
                {
                    "op_type": "strided_slice",
                    "op_inputs": {"Input": ["input_data"]},
                    "op_outputs": {"Out": ["slice_output_data"]},
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={},
                inputs={
                    "input_data": TensorConfig(
                        data_gen=partial(generate_input1, dics)
                    )
                },
                outputs=["slice_output_data"],
            )

            yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> Generator[
        Any, Any, tuple[paddle_infer.Config, list[int], float] | None
    ]:
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 56, 56, 192]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [8, 56, 56, 192]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [4, 56, 56, 192]
            }

        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            inputs = program_config.inputs

            if dynamic_shape:
                for i in range(len(attrs[0]["starts"])):
                    if attrs[0]["starts"][i] < 0 or attrs[0]["ends"][i] < 0:
                        return 0, 3
            if not dynamic_shape:
                for x in attrs[0]["axes"]:
                    if x == 0:
                        return 0, 3
            ver = paddle_infer.get_trt_compile_version()
            if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000:
                return 0, 3
            return 1, 2

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, False),
            1e-5,
        )

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-5,
        )

    def test(self):
        self.run_test()


class TrtConvertStridedSliceTest2(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]]):
            return np.random.random([1, 56, 56, 192]).astype(np.float32)

        for axes, starts, ends, decrease_axis, infer_flags, strides in product(
            [[1, 2], [2, 3], [1, 3]],
            [[-10, 1], [-10, 20], [-10, 15], [-10, 16], [-10, 20]],
            [[-9, 10000], [-9, -1], [-9, 40]],
            [[]],
            [[1, 1]],
            [[2, 2]],
        ):
            dics = [
                {
                    "axes": axes,
                    "starts": starts,
                    "ends": ends,
                    "decrease_axis": [axes[0]],
                    "infer_flags": infer_flags,
                    "strides": strides,
                }
            ]

            ops_config = [
                {
                    "op_type": "strided_slice",
                    "op_inputs": {"Input": ["input_data"]},
                    "op_outputs": {"Out": ["slice_output_data"]},
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={},
                inputs={
                    "input_data": TensorConfig(
                        data_gen=partial(generate_input1, dics)
                    )
                },
                outputs=["slice_output_data"],
            )

            yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> Generator[
        Any, Any, tuple[paddle_infer.Config, list[int], float] | None
    ]:
        def generate_dynamic_shape():
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 56, 56, 192]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [8, 100, 100, 200]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [4, 56, 56, 192]
            }

        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), (1, 2), 1e-5

        # for dynamic_shape
        generate_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), (1, 2), 1e-5

    def test(self):
        self.run_test()


if __name__ == "__main__":
    unittest.main()
